The so-called “resting state” of a brain actually consists of a high level of activity and is estimated to account for approximately 95% of all brain energy consumption. A better terminology may be therefore the brain default mode network (DMN) which is believed to be the way that the brain organizes memory and prepares for future action (Raichle, M E, The Brain's Dark Energy, Scientific American, 2010). Disruptions to the DMN may underlie brain disorders like Alzheimer's, Parkinson's, depression, etc. Likewise, efficacious treatment of brain disorders possibly involves some level of restoration of DMN function.
New neuro-interventions are under development (i.e., neuromodulation therapies) that make use of brain-pacemakers, drug-delivery devices, or other implants to stimulate, activate, block or modulate certain neuronal targets in the brain with the purpose to reduce disease symptoms. The therapeutic mode of action of these novel therapies is usually a modification in neuronal network activity patterns leading to beneficial results. Such devices bear great promise to treat large patient groups who otherwise would receive sub-optimal therapies.
State-of-the-art targeting software uses anatomical patient images to determine the optimum site for neuromodulation therapy delivery. Sometimes this is augmented with local electrophysiological information for target-refinement. However, the therapeutic workings of neuromodulation devices are essentially occurring on a more global neuronal network level and this information cannot be captured by anatomical imaging and local electrophysiological exploration alone. For optimum treatment planning and execution, network aspects should be integrated.
It is therefore an object of the present invention to improve a device for planning a neuromodulation therapy, especially in that the device for planning a neuromodulation therapy is able to integrate network aspects to optimize treatment planning and execution of a neuromodulation therapy.
Accordingly, a device for planning a neuromodulation therapy comprises receiving means to receive brain default mode network data of a patient, template means comprising a template brain default mode network data, normalizing means being configured such that normalized patient brain default mode network data can be prepared on the basis on the received brain default mode network data and the template brain default mode network data, storage means comprising a brain default mode network database, and comparison means configured such that the normalized patient brain default mode network data and the data contained in the brain default mode network database can be compared.
By this, the advantage is achieved that network aspects can be integrated in the planning of the neuromodulation therapy and thus treatment planning and execution of a neuromodulation therapy can be improved.
Such a device may make use of a DMN database, e.g. an age-controlled healthy DMN, pathological DMN like Alzheimer's, Parkinson, depression, etc. to diagnose a patient's particular DMN and to couple this to an optimum neuromodulation treatment strategy. This coupling may be as simple as a look-up table that relates a DMN-diagnosis to one or more particular neuromodulation strategies, but it may also consist of a more advanced module in which DMN changes during various neuromodulation strategies are simulated and from which results a best outcome is selected automatically or manually by a user, i.e. by a physician.
Especially, the neuromodulation therapy may be a deep brain stimulation (DBS) therapy, i.e. an effective and accepted treatment involving the implantation of a “brain pacemaker”, which sends mild electrical impulses to specific parts of the brain.
In a preferred embodiment the comparison means may be configured such that, based on the spatially normalized patient brain default mode network data and the data contained in the brain default mode network database, a DMN deviation map of the patient can be prepared and based on this DMN deviation map a pathology classification can be done, e.g., by a physician.
In a further preferred embodiment it is possible that the device comprises enhancement means comprising receiving means to receive anatomical data of the patient, preferably magnetic resonance data or magnetic resonance imaging data, and transferring means configured such that the anatomical data can be transferred to the normalizing means. By this, the advantage is achieved that the accuracy of the planning of the neuromodulation therapy can be enhanced due to the fact that the brain default mode network data of a patient and the template brain default mode network data are augmented by the anatomical data of the patient. Consequently, more data and information about the patient and the actual situation are available and thus a better planning result is possible.
Additionally, the device may further comprise mapping means configured such that a brain default mode network deviation map for the patient can be provided, whereby the deviation map preferably comprises a pathology classification.
It is possible that the device further comprises planning means configured such that at least one implant position for a neuromodulation therapy, preferably a deep brain stimulation therapy, can be planned. Exemplarily, based on the comparison of the spatially normalized patient brain default mode network data and the data contained in the brain default mode network database the optimal therapy targets can be identified and thereby also an identification of implant positions for electrodes for a neuromodulation therapy can be identified. It is commonly accepted that implant positions should be planned beforehand and also with high accuracy to achieve a good basis for the implant procedure itself. Based on a sufficient planning of the implant position, an implant may be implanted with high accuracy.
Thus, especially by the planning means the advantage is achieved that e.g. the implant position of a probe of a neuromodulation system can be planned with highest possible accuracy and thereby the basis is made for an accurate implantation of the probe, which is the further basis for a high accurate neuromodulation therapy.
Further preferably, the device further comprises simulation means configured such that at least one implant position can be chosen and its effects can be simulated. Thereby, the advantage is achieved that e.g. based on the planned implant position(s) the effects of a neuromodulation conducted by the implanted probes can be simulated and thus the efficiency and the accuracy of the neuromodulation can be checked. Advantageously, the effects of the planned neuromodulation can be tested and also e.g. unwanted side-effects can be detected and eliminated.
In a further preferred embodiment it is possible that the device further comprises display means configured such that the comparison conducted by the comparison means can be displayed. The display means may be e.g. a display on which the DMN deviation map of the patient and/or the planning of the implant positions and/or the simulation results can be displayed. The display means can also comprise a touch screen.
Furthermore, the device may comprise input means for inputting data and/or outputting means for outputting data. The input means can e.g. comprise a normal keyboard and/or touch screen and the output data can e.g. comprise beside any display means sound outputting means, preferably at least a loudspeaker.
Additionally, it is possible that the device comprises at least a workstation, whereby on the workstation at least one of the receiving means, the template means, the normalizing means, the brain default mode network database, the comparison means, the enhancement means, the mapping means, the planning means, and/or the simulation means is installed.
Furthermore, it is possible that the normalizing means are configured such that spatially normalized patient brain default mode network data can be prepared on the basis on the received brain default mode network data and/or that the normalized patient brain default mode network data are spatially normalized patient brain default mode network data.
Moreover, it is possible the device comprises checking and/or fine-tuning means configured such that brain default mode network data of a patient a neuromodulation therapy can be checked and/or used to fine-tune the planned neuromodulation program. Thereby, the advantage is achieved that in the post-operative phase real brain default mode network data of a patient can be used to check and fine-tune the planned neuromodulation therapy, which is preferably a deep brain stimulation therapy. E.g., brain default mode network data of a patient with one or more active implants for conducting the neuromodulation therapy can be used to assess the real effects and to conduct a fine-tuning of the neuromodulation therapy by, e.g., adjusting the therapy parameters if necessary.